


'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							tempfile.mkdtemp()

          __A	:       Tuple      =							[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "的",
              "价",
              "格",
              "是",
              "15",
              "便",
              "alex",
              "##andra",
              "，",
              "。",
              "-",
              "t",
              "shirt",
          ]
          __A	:       Optional[Any]      =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]		)
          with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

          __A	:       Any      =							{
              "do_resize": True,
              "size": {"height": 224, "width": 224},
              "do_center_crop": True,
              "crop_size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
              "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
              "do_convert_rgb": True,
          }
          __A	:       Dict      =							os.path.join(self.tmpdirname			,     __snake_case		)
          with open(self.image_processor_file			,     "w"			,     encoding="utf-8"		) as fp:
               json.dump(__snake_case			,     __snake_case		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizer.from_pretrained(self.tmpdirname			,     **__snake_case		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizerFast.from_pretrained(self.tmpdirname			,     **__snake_case		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname			,     **__snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          shutil.rmtree(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta		)]

          __A	:       Any      =							[Image.fromarray(np.moveaxis(__snake_case			,     0			,     -1		)		) for x in image_inputs]

          return image_inputs


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.get_tokenizer()
          __A	:       List[str]      =							self.get_rust_tokenizer()
          __A	:       str      =							self.get_image_processor()

          __A	:       int      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)
          processor_slow.save_pretrained(self.tmpdirname		)
          __A	:       Optional[Any]      =							ChineseCLIPProcessor.from_pretrained(self.tmpdirname			,     use_fast=__snake_case		)

          __A	:       List[str]      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)
          processor_fast.save_pretrained(self.tmpdirname		)
          __A	:       str      =							ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

          self.assertEqual(processor_slow.tokenizer.get_vocab()			,     tokenizer_slow.get_vocab()		)
          self.assertEqual(processor_fast.tokenizer.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertEqual(tokenizer_slow.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertIsInstance(processor_slow.tokenizer			,     __snake_case		)
          self.assertIsInstance(processor_fast.tokenizer			,     __snake_case		)

          self.assertEqual(processor_slow.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertEqual(processor_fast.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertIsInstance(processor_slow.image_processor			,     __snake_case		)
          self.assertIsInstance(processor_fast.image_processor			,     __snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()		)
          processor.save_pretrained(self.tmpdirname		)

          __A	:       Optional[int]      =							self.get_tokenizer(cls_token="(CLS)"			,     sep_token="(SEP)"		)
          __A	:       Any      =							self.get_image_processor(do_normalize=__snake_case		)

          __A	:       Dict      =							ChineseCLIPProcessor.from_pretrained(
              self.tmpdirname			,     cls_token="(CLS)"			,     sep_token="(SEP)"			,     do_normalize=__snake_case		)

          self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer_add_kwargs.get_vocab()		)
          self.assertIsInstance(processor.tokenizer			,     __snake_case		)

          self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()		)
          self.assertIsInstance(processor.image_processor			,     __snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.get_image_processor()
          __A	:       Optional[Any]      =							self.get_tokenizer()

          __A	:       str      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)

          __A	:       Union[str, Any]      =							self.prepare_image_inputs()

          __A	:       List[str]      =							image_processor(__snake_case			,     return_tensors="np"		)
          __A	:       int      =							processor(images=__snake_case			,     return_tensors="np"		)

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.get_image_processor()
          __A	:       int      =							self.get_tokenizer()

          __A	:       List[str]      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)

          __A	:       List[str]      =							"Alexandra，T-shirt的价格是15便士。"

          __A	:       List[Any]      =							processor(text=__snake_case		)

          __A	:       Optional[int]      =							tokenizer(__snake_case		)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_image_processor()
          __A	:       Dict      =							self.get_tokenizer()

          __A	:       Any      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)

          __A	:       str      =							"Alexandra，T-shirt的价格是15便士。"
          __A	:       Optional[int]      =							self.prepare_image_inputs()

          __A	:       List[str]      =							processor(text=__snake_case			,     images=__snake_case		)

          self.assertListEqual(list(inputs.keys()		)			,     ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

          # test if it raises when no input is passed
          with pytest.raises(__snake_case		):
               processor()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							self.get_image_processor()
          __A	:       Optional[int]      =							self.get_tokenizer()

          __A	:       Optional[int]      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)

          __A	:       Any      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A	:       Tuple      =							processor.batch_decode(__snake_case		)
          __A	:       int      =							tokenizer.batch_decode(__snake_case		)

          self.assertListEqual(__snake_case			,     __snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.get_image_processor()
          __A	:       int      =							self.get_tokenizer()

          __A	:       int      =							ChineseCLIPProcessor(tokenizer=__snake_case			,     image_processor=__snake_case		)

          __A	:       Tuple      =							"Alexandra，T-shirt的价格是15便士。"
          __A	:       int      =							self.prepare_image_inputs()

          __A	:       Union[str, Any]      =							processor(text=__snake_case			,     images=__snake_case		)

          self.assertListEqual(list(inputs.keys()		)			,     processor.model_input_names		)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




